Fast efficient spectrum allocation and heterogeneous network selection based on modified dynamic evolutionary game

Dynamic evolutionary game has attracted a lot of attention in cognitive network because it can adaptively learn during the strategy under changing conditions adopting replicator dynamics equation. But the information required by the original replicator dynamics equation is large. In this paper, we provide a modified replicator dynamics equation, which can adaptively converge to the desired stable state with faster speed. Noted that, the necessary information transmission in the evolutionary process is much less than that of the original replicator dynamics equation. Moreover, we apply the modified dynamics equation to (i) opportunistic spectrum access with multiple primary users selling free spectrum opportunities to multiple secondary users; (ii) heterogeneous network selection. Simulation results show that the evolving time is cut down greatly and equal maximal payoff is obtained. Besides, the proposed method is robust even if there is large time delay in the process of information transmission.